Charge-Free Mixing Entropy Battery Enabled by Low-Cost Electrode Materials
Bibliographic record
Abstract
Salinity gradients are a vast and untapped energy resource. For every cubic meter of freshwater that mixes with seawater, approximately 0.65 kW h of theoretically recoverable energy is lost. For coastal wastewater treatment plants that discharge to the ocean, this energy, if recovered, could power the plant. The mixing entropy battery (MEB) uses battery electrodes to convert salinity gradient energy into electricity in a four-step process: (1) freshwater exchange; (2) charging in freshwater; (3) seawater exchange; and (4) discharging in seawater. Previously, we demonstrated a proof of concept, but with electrode materials that required an energy investment during the charging step. Here, we introduce a charge-free MEB with low-cost electrodes: Prussian Blue (PB) and polypyrrole (PPy). Importantly, this MEB requires no energy investment, and the electrode materials are stable with repeated cycling. The MEB equipped with PB and PPy achieved high voltage ratios (actual voltages obtained divided by the theoretical voltages) of 89.5% in wastewater effluent and 97.6% in seawater, with over 93% capacity retention after 50 cycles of operation and 97-99% over 150 cycles with a polyvinyl alcohol/sulfosuccinic acid (PVA/SSA) coating on the PB electrode.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".